{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Implementing a Neural Network\n",
    "In this exercise we will develop a neural network with fully-connected layers to perform classification, and test it out on the CIFAR-10 dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# A bit of setup\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from cs231n.classifiers.neural_net import TwoLayerNet\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "    \"\"\" returns relative error \"\"\"\n",
    "    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "We will use the class `TwoLayerNet` in the file `cs231n/classifiers/neural_net.py` to represent instances of our network. The network parameters are stored in the instance variable `self.params` where keys are string parameter names and values are numpy arrays. Below, we initialize toy data and a toy model that we will use to develop your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# Create a small net and some toy data to check your implementations.\n",
    "# Note that we set the random seed for repeatable experiments.\n",
    "\n",
    "input_size = 4\n",
    "hidden_size = 10\n",
    "num_classes = 3\n",
    "num_inputs = 5\n",
    "\n",
    "def init_toy_model():\n",
    "    np.random.seed(0)\n",
    "    return TwoLayerNet(input_size, hidden_size, num_classes, std=1e-1)\n",
    "\n",
    "def init_toy_data():\n",
    "    np.random.seed(1)\n",
    "    X = 10 * np.random.randn(num_inputs, input_size)\n",
    "    y = np.array([0, 1, 2, 2, 1])\n",
    "    return X, y\n",
    "\n",
    "net = init_toy_model()\n",
    "X, y = init_toy_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forward pass: compute scores\n",
    "Open the file `cs231n/classifiers/neural_net.py` and look at the method `TwoLayerNet.loss`. This function is very similar to the loss functions you have written for the SVM and Softmax exercises: It takes the data and weights and computes the class scores, the loss, and the gradients on the parameters. \n",
    "\n",
    "Implement the first part of the forward pass which uses the weights and biases to compute the scores for all inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your scores:\n",
      "[[-0.81233741 -1.27654624 -0.70335995]\n",
      " [-0.17129677 -1.18803311 -0.47310444]\n",
      " [-0.51590475 -1.01354314 -0.8504215 ]\n",
      " [-0.15419291 -0.48629638 -0.52901952]\n",
      " [-0.00618733 -0.12435261 -0.15226949]]\n",
      "\n",
      "correct scores:\n",
      "[[-0.81233741 -1.27654624 -0.70335995]\n",
      " [-0.17129677 -1.18803311 -0.47310444]\n",
      " [-0.51590475 -1.01354314 -0.8504215 ]\n",
      " [-0.15419291 -0.48629638 -0.52901952]\n",
      " [-0.00618733 -0.12435261 -0.15226949]]\n",
      "\n",
      "Difference between your scores and correct scores:\n",
      "3.6802720496109664e-08\n"
     ]
    }
   ],
   "source": [
    "scores = net.loss(X)\n",
    "print('Your scores:')\n",
    "print(scores)\n",
    "print()\n",
    "print('correct scores:')\n",
    "correct_scores = np.asarray([\n",
    "  [-0.81233741, -1.27654624, -0.70335995],\n",
    "  [-0.17129677, -1.18803311, -0.47310444],\n",
    "  [-0.51590475, -1.01354314, -0.8504215 ],\n",
    "  [-0.15419291, -0.48629638, -0.52901952],\n",
    "  [-0.00618733, -0.12435261, -0.15226949]])\n",
    "print(correct_scores)\n",
    "print()\n",
    "\n",
    "# The difference should be very small. We get < 1e-7\n",
    "print('Difference between your scores and correct scores:')\n",
    "print(np.sum(np.abs(scores - correct_scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forward pass: compute loss\n",
    "In the same function, implement the second part that computes the data and regularization loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference between your loss and correct loss:\n",
      "1.7985612998927536e-13\n"
     ]
    }
   ],
   "source": [
    "loss, _ = net.loss(X, y, reg=0.05)\n",
    "correct_loss = 1.30378789133\n",
    "\n",
    "# should be very small, we get < 1e-12\n",
    "print('Difference between your loss and correct loss:')\n",
    "print(np.sum(np.abs(loss - correct_loss)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Backward pass\n",
    "Implement the rest of the function. This will compute the gradient of the loss with respect to the variables `W1`, `b1`, `W2`, and `b2`. Now that you (hopefully!) have a correctly implemented forward pass, you can debug your backward pass using a numeric gradient check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 max relative error: 3.561318e-09\n",
      "b1 max relative error: 1.555470e-09\n",
      "W2 max relative error: 3.440708e-09\n",
      "b2 max relative error: 3.865091e-11\n"
     ]
    }
   ],
   "source": [
    "from cs231n.gradient_check import eval_numerical_gradient\n",
    "\n",
    "# Use numeric gradient checking to check your implementation of the backward pass.\n",
    "# If your implementation is correct, the difference between the numeric and\n",
    "# analytic gradients should be less than 1e-8 for each of W1, W2, b1, and b2.\n",
    "\n",
    "loss, grads = net.loss(X, y, reg=0.05)\n",
    "\n",
    "# these should all be less than 1e-8 or so\n",
    "for param_name in grads:\n",
    "    f = lambda W: net.loss(X, y, reg=0.05)[0]\n",
    "    param_grad_num = eval_numerical_gradient(f, net.params[param_name], verbose=False)\n",
    "    print('%s max relative error: %e' % (param_name, rel_error(param_grad_num, grads[param_name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train the network\n",
    "To train the network we will use stochastic gradient descent (SGD), similar to the SVM and Softmax classifiers. Look at the function `TwoLayerNet.train` and fill in the missing sections to implement the training procedure. This should be very similar to the training procedure you used for the SVM and Softmax classifiers. You will also have to implement `TwoLayerNet.predict`, as the training process periodically performs prediction to keep track of accuracy over time while the network trains.\n",
    "\n",
    "Once you have implemented the method, run the code below to train a two-layer network on toy data. You should achieve a training loss less than 0.02."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "final_training_loss"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final training loss:  0.017149607938732093\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "net = init_toy_model()\n",
    "stats = net.train(X, y, X, y,\n",
    "            learning_rate=1e-1, reg=5e-6,\n",
    "            num_iters=100, verbose=False)\n",
    "\n",
    "print('Final training loss: ', stats['loss_history'][-1])\n",
    "\n",
    "# plot the loss history\n",
    "plt.plot(stats['loss_history'])\n",
    "plt.xlabel('iteration')\n",
    "plt.ylabel('training loss')\n",
    "plt.title('Training Loss history')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the data\n",
    "Now that you have implemented a two-layer network that passes gradient checks and works on toy data, it's time to load up our favorite CIFAR-10 data so we can use it to train a classifier on a real dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 3072)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 3072)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "from cs231n.data_utils import load_CIFAR10\n",
    "\n",
    "def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000):\n",
    "    \"\"\"\n",
    "    Load the CIFAR-10 dataset from disk and perform preprocessing to prepare\n",
    "    it for the two-layer neural net classifier. These are the same steps as\n",
    "    we used for the SVM, but condensed to a single function.  \n",
    "    \"\"\"\n",
    "    # Load the raw CIFAR-10 data\n",
    "    cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "    \n",
    "    # Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "    try:\n",
    "       del X_train, y_train\n",
    "       del X_test, y_test\n",
    "       print('Clear previously loaded data.')\n",
    "    except:\n",
    "       pass\n",
    "\n",
    "    X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "        \n",
    "    # Subsample the data\n",
    "    mask = list(range(num_training, num_training + num_validation))\n",
    "    X_val = X_train[mask]\n",
    "    y_val = y_train[mask]\n",
    "    mask = list(range(num_training))\n",
    "    X_train = X_train[mask]\n",
    "    y_train = y_train[mask]\n",
    "    mask = list(range(num_test))\n",
    "    X_test = X_test[mask]\n",
    "    y_test = y_test[mask]\n",
    "\n",
    "    # Normalize the data: subtract the mean image\n",
    "    mean_image = np.mean(X_train, axis=0)\n",
    "    X_train -= mean_image\n",
    "    X_val -= mean_image\n",
    "    X_test -= mean_image\n",
    "\n",
    "    # Reshape data to rows\n",
    "    X_train = X_train.reshape(num_training, -1)\n",
    "    X_val = X_val.reshape(num_validation, -1)\n",
    "    X_test = X_test.reshape(num_test, -1)\n",
    "\n",
    "    return X_train, y_train, X_val, y_val, X_test, y_test\n",
    "\n",
    "\n",
    "# Invoke the above function to get our data.\n",
    "X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train a network\n",
    "To train our network we will use SGD. In addition, we will adjust the learning rate with an exponential learning rate schedule as optimization proceeds; after each epoch, we will reduce the learning rate by multiplying it by a decay rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1000: loss 2.302585\n",
      "iteration 100 / 1000: loss 2.020340\n",
      "iteration 200 / 1000: loss 1.765165\n",
      "iteration 300 / 1000: loss 1.681722\n",
      "iteration 400 / 1000: loss 1.819264\n",
      "iteration 500 / 1000: loss 1.615274\n",
      "iteration 600 / 1000: loss 1.494634\n",
      "iteration 700 / 1000: loss 1.639707\n",
      "iteration 800 / 1000: loss 1.465098\n",
      "iteration 900 / 1000: loss 1.682184\n",
      "Validation accuracy:  0.472\n"
     ]
    }
   ],
   "source": [
    "input_size = 32 * 32 * 3\n",
    "hidden_size = 64\n",
    "num_classes = 10\n",
    "net = TwoLayerNet(input_size, hidden_size, num_classes)\n",
    "\n",
    "# Train the network\n",
    "stats = net.train(X_train, y_train, X_val, y_val,\n",
    "            num_iters=1000, batch_size=200,\n",
    "            learning_rate=7.5e-4, learning_rate_decay=0.95,\n",
    "            reg=1e-3, verbose=True)\n",
    "\n",
    "# Predict on the validation set\n",
    "val_acc = (net.predict(X_val) == y_val).mean()\n",
    "print('Validation accuracy: ', val_acc)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Debug the training\n",
    "With the default parameters we provided above, you should get a validation accuracy of about 0.29 on the validation set. This isn't very good.\n",
    "\n",
    "One strategy for getting insight into what's wrong is to plot the loss function and the accuracies on the training and validation sets during optimization.\n",
    "\n",
    "Another strategy is to visualize the weights that were learned in the first layer of the network. In most neural networks trained on visual data, the first layer weights typically show some visible structure when visualized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the loss function and train / validation accuracies\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.plot(stats['loss_history'])\n",
    "plt.title('Loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Loss')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.plot(stats['train_acc_history'], label='train')\n",
    "plt.plot(stats['val_acc_history'], label='val')\n",
    "plt.title('Classification accuracy history')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Classification accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from cs231n.vis_utils import visualize_grid\n",
    "\n",
    "# Visualize the weights of the network\n",
    "\n",
    "def show_net_weights(net):\n",
    "    W1 = net.params['W1']\n",
    "    W1 = W1.reshape(32, 32, 3, -1).transpose(3, 0, 1, 2)\n",
    "    plt.imshow(visualize_grid(W1, padding=3).astype('uint8'))\n",
    "    plt.gca().axis('off')\n",
    "    plt.show()\n",
    "\n",
    "show_net_weights(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tune your hyperparameters\n",
    "\n",
    "**What's wrong?**. Looking at the visualizations above, we see that the loss is decreasing more or less linearly, which seems to suggest that the learning rate may be too low. Moreover, there is no gap between the training and validation accuracy, suggesting that the model we used has low capacity, and that we should increase its size. On the other hand, with a very large model we would expect to see more overfitting, which would manifest itself as a very large gap between the training and validation accuracy.\n",
    "\n",
    "**Tuning**. Tuning the hyperparameters and developing intuition for how they affect the final performance is a large part of using Neural Networks, so we want you to get a lot of practice. Below, you should experiment with different values of the various hyperparameters, including hidden layer size, learning rate, numer of training epochs, and regularization strength. You might also consider tuning the learning rate decay, but you should be able to get good performance using the default value.\n",
    "\n",
    "**Approximate results**. You should be aim to achieve a classification accuracy of greater than 48% on the validation set. Our best network gets over 52% on the validation set.\n",
    "\n",
    "**Experiment**: You goal in this exercise is to get as good of a result on CIFAR-10 as you can (52% could serve as a reference), with a fully-connected Neural Network. Feel free implement your own techniques (e.g. PCA to reduce dimensionality, or adding dropout, or adding features to the solver, etc.)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Explain your hyperparameter tuning process below.**\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$\n",
    "\n",
    "- 默认参数：**hidden_size=50**, **reg=0.25**, **num_iters=1000**, batch_size=200, lr_decay=0.95, **lr=1e-4**\n",
    "- （加粗部分进行了调整）\n",
    "\n",
    "\n",
    "- **learning_rate**:\n",
    "- 首先从用reg=1e-6，缩小lr区间，使得应用区间内lr时loss均下降，并画出loss-#iteration（比如100个iteration记录一次loss）曲线观察\n",
    "- 从[1e-8, 1e-7,1e-6,1e-5,1e-4,1e-3,1e-2]到[1e-4, 1e-3]（太小则loss高水平跳动，太大则loss为nan）\n",
    "- 最终细化，最终搜索区间lr_interval=[2.5e-4, 5e-4, 7.5e-4, 1e-3, 2.5e-3]\n",
    "- 他们都能在val上达到0.39+，而且train_acc和val_acc之间没有过大的gap\n",
    "\n",
    "\n",
    "- **regularization strength**:\n",
    "- use double nested loop to tune reg based on lr_interval\n",
    "- 从[1e-2, 1e-1, 1, 1e1, 1e2, 1e3, 1e4]到[1e-2, 1e-1, 1, 1e1]\n",
    "  - 1e2及以后的train_acc和val_acc相比前面的reg的两个acc都有跳崖下跌，去除reg=1e2, 1e3, 1e4\n",
    "  - 几乎在所有lr上，reg=1e1的val_acc都比其他reg逊色，去除reg=1e1\n",
    "  - 在很多lr上，reg=1e-2的val_acc有接近/就是最优值，考虑扩展reg左边界，添加reg=1e-4, 1e-3\n",
    "  - 同时发现lr=2.5e-4的所有reg相比其他lr的所有reg都逊色，val_acc没有达到0.4（而其他都有），考虑去除lr=2.5e-4\n",
    "- 再到[1e-4, 1e-3, 1e-2, 1e-1, 1]\n",
    "  - val_acc最优值对应组合均在lr和reg区间内达到，故不再考虑拓展左边界\n",
    "  - 同时观察到，所有lr上最大值基本都在1e-4到1e-2上达到，考虑在[1e-4, 1e-2]上细化reg区间\n",
    "- 最终细化，最终搜索区间reg_interval=[1e-4, 5e-4, 1e-3, 5e-3, 1e-2]\n",
    "  - 注意此时lr_interval=[5e-4, 7.5e-4, 1e-3, 2.5e-3]\n",
    "\n",
    "\n",
    "- **hidden_size**:\n",
    "- use triple nested loop to tune reg based on lr_interval and reg_interval\n",
    "- 从[8, 16, 32, 64, 128]到[32, 48, 64, 80, 96, 128]\n",
    "  - 观察中间结果，发现32以下的比32和64逊色不少\n",
    "\n",
    "\n",
    "- **numbers of epochs**:\n",
    "- 最后发现64、80、96上均有较好的val_accurancy，再提高epoch对比（hidden_size大可能有隐藏的过拟合问题？）\n",
    "  - hidden_size=64, reg=1e-3, num_iters=1000, lr=7.5e-4\n",
    "  > - val=0.498, test=0.473, test=0.491~0.503（#iter=1960，大概8个epochs）  \n",
    "  > - loss图像中train和val之间的gap比较小（与下面二者相比发现两条线更靠近）\n",
    "  - hidden_size=80, reg=1e-2, num_iters=1000, lr=1e-3\n",
    "  > - val=0.500, test=0.478, test=0.48~0.508（#iter=1960，大概8个epochs）  \n",
    "  > - loss图像中train和val之间的gap比较大\n",
    "  - hidden_size=96, reg=1e-3, num_iters=1000, lr=2.5e-3\n",
    "  > - val=0.501, test=0.476, test=0.479~0.48（#iter=1960，大概8个epochs）\n",
    "  > - loss图像中train和val之间的gap比较大（与hidden_size=80的差不多）\n",
    "  - 其实是否应该每个hidden_size的参数都提高epochs看效果？\n",
    "- 最后选**hidden_size=80**的吧，能跑到**test_acc=0.51**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr 5.000000e-04 / reg 1.000000e-04 / h 48 / train_accurancy 0.450245 / val_accurancy 0.450000\n",
      "lr 5.000000e-04 / reg 5.000000e-04 / h 48 / train_accurancy 0.447367 / val_accurancy 0.457000\n",
      "lr 5.000000e-04 / reg 1.000000e-03 / h 48 / train_accurancy 0.450184 / val_accurancy 0.440000\n",
      "lr 5.000000e-04 / reg 5.000000e-03 / h 48 / train_accurancy 0.453673 / val_accurancy 0.456000\n",
      "lr 5.000000e-04 / reg 1.000000e-02 / h 48 / train_accurancy 0.447020 / val_accurancy 0.451000\n",
      "lr 7.500000e-04 / reg 1.000000e-04 / h 48 / train_accurancy 0.482224 / val_accurancy 0.469000\n",
      "lr 7.500000e-04 / reg 5.000000e-04 / h 48 / train_accurancy 0.477327 / val_accurancy 0.468000\n",
      "lr 7.500000e-04 / reg 1.000000e-03 / h 48 / train_accurancy 0.477306 / val_accurancy 0.465000\n",
      "lr 7.500000e-04 / reg 5.000000e-03 / h 48 / train_accurancy 0.477082 / val_accurancy 0.457000\n",
      "lr 7.500000e-04 / reg 1.000000e-02 / h 48 / train_accurancy 0.479755 / val_accurancy 0.467000\n",
      "lr 1.000000e-03 / reg 1.000000e-04 / h 48 / train_accurancy 0.489163 / val_accurancy 0.457000\n",
      "lr 1.000000e-03 / reg 5.000000e-04 / h 48 / train_accurancy 0.485061 / val_accurancy 0.448000\n",
      "lr 1.000000e-03 / reg 1.000000e-03 / h 48 / train_accurancy 0.492939 / val_accurancy 0.464000\n",
      "lr 1.000000e-03 / reg 5.000000e-03 / h 48 / train_accurancy 0.486857 / val_accurancy 0.462000\n",
      "lr 1.000000e-03 / reg 1.000000e-02 / h 48 / train_accurancy 0.497959 / val_accurancy 0.487000\n",
      "lr 2.500000e-03 / reg 1.000000e-04 / h 48 / train_accurancy 0.492939 / val_accurancy 0.467000\n",
      "lr 2.500000e-03 / reg 5.000000e-04 / h 48 / train_accurancy 0.485184 / val_accurancy 0.472000\n",
      "lr 2.500000e-03 / reg 1.000000e-03 / h 48 / train_accurancy 0.462531 / val_accurancy 0.459000\n",
      "lr 2.500000e-03 / reg 5.000000e-03 / h 48 / train_accurancy 0.482510 / val_accurancy 0.451000\n",
      "lr 2.500000e-03 / reg 1.000000e-02 / h 48 / train_accurancy 0.500898 / val_accurancy 0.471000\n",
      "lr 5.000000e-04 / reg 1.000000e-04 / h 80 / train_accurancy 0.456959 / val_accurancy 0.449000\n",
      "lr 5.000000e-04 / reg 5.000000e-04 / h 80 / train_accurancy 0.461633 / val_accurancy 0.452000\n",
      "lr 5.000000e-04 / reg 1.000000e-03 / h 80 / train_accurancy 0.463551 / val_accurancy 0.446000\n",
      "lr 5.000000e-04 / reg 5.000000e-03 / h 80 / train_accurancy 0.459673 / val_accurancy 0.447000\n",
      "lr 5.000000e-04 / reg 1.000000e-02 / h 80 / train_accurancy 0.455000 / val_accurancy 0.444000\n",
      "lr 7.500000e-04 / reg 1.000000e-04 / h 80 / train_accurancy 0.488837 / val_accurancy 0.467000\n",
      "lr 7.500000e-04 / reg 5.000000e-04 / h 80 / train_accurancy 0.484429 / val_accurancy 0.467000\n",
      "lr 7.500000e-04 / reg 1.000000e-03 / h 80 / train_accurancy 0.486714 / val_accurancy 0.471000\n",
      "lr 7.500000e-04 / reg 5.000000e-03 / h 80 / train_accurancy 0.487286 / val_accurancy 0.468000\n",
      "lr 7.500000e-04 / reg 1.000000e-02 / h 80 / train_accurancy 0.488694 / val_accurancy 0.479000\n",
      "lr 1.000000e-03 / reg 1.000000e-04 / h 80 / train_accurancy 0.509306 / val_accurancy 0.490000\n",
      "lr 1.000000e-03 / reg 5.000000e-04 / h 80 / train_accurancy 0.504816 / val_accurancy 0.480000\n",
      "lr 1.000000e-03 / reg 1.000000e-03 / h 80 / train_accurancy 0.500796 / val_accurancy 0.461000\n",
      "lr 1.000000e-03 / reg 5.000000e-03 / h 80 / train_accurancy 0.501898 / val_accurancy 0.481000\n",
      "lr 1.000000e-03 / reg 1.000000e-02 / h 80 / train_accurancy 0.505388 / val_accurancy 0.500000\n",
      "lr 2.500000e-03 / reg 1.000000e-04 / h 80 / train_accurancy 0.492755 / val_accurancy 0.452000\n",
      "lr 2.500000e-03 / reg 5.000000e-04 / h 80 / train_accurancy 0.508653 / val_accurancy 0.462000\n",
      "lr 2.500000e-03 / reg 1.000000e-03 / h 80 / train_accurancy 0.520714 / val_accurancy 0.491000\n",
      "lr 2.500000e-03 / reg 5.000000e-03 / h 80 / train_accurancy 0.459204 / val_accurancy 0.438000\n",
      "lr 2.500000e-03 / reg 1.000000e-02 / h 80 / train_accurancy 0.499061 / val_accurancy 0.471000\n",
      "lr 5.000000e-04 / reg 1.000000e-04 / h 96 / train_accurancy 0.459143 / val_accurancy 0.453000\n",
      "lr 5.000000e-04 / reg 5.000000e-04 / h 96 / train_accurancy 0.461694 / val_accurancy 0.454000\n",
      "lr 5.000000e-04 / reg 1.000000e-03 / h 96 / train_accurancy 0.461408 / val_accurancy 0.462000\n",
      "lr 5.000000e-04 / reg 5.000000e-03 / h 96 / train_accurancy 0.462694 / val_accurancy 0.468000\n",
      "lr 5.000000e-04 / reg 1.000000e-02 / h 96 / train_accurancy 0.460714 / val_accurancy 0.461000\n",
      "lr 7.500000e-04 / reg 1.000000e-04 / h 96 / train_accurancy 0.493429 / val_accurancy 0.475000\n",
      "lr 7.500000e-04 / reg 5.000000e-04 / h 96 / train_accurancy 0.492571 / val_accurancy 0.489000\n",
      "lr 7.500000e-04 / reg 1.000000e-03 / h 96 / train_accurancy 0.491449 / val_accurancy 0.473000\n",
      "lr 7.500000e-04 / reg 5.000000e-03 / h 96 / train_accurancy 0.492020 / val_accurancy 0.476000\n",
      "lr 7.500000e-04 / reg 1.000000e-02 / h 96 / train_accurancy 0.488980 / val_accurancy 0.491000\n",
      "lr 1.000000e-03 / reg 1.000000e-04 / h 96 / train_accurancy 0.505367 / val_accurancy 0.488000\n",
      "lr 1.000000e-03 / reg 5.000000e-04 / h 96 / train_accurancy 0.504143 / val_accurancy 0.473000\n",
      "lr 1.000000e-03 / reg 1.000000e-03 / h 96 / train_accurancy 0.507388 / val_accurancy 0.485000\n",
      "lr 1.000000e-03 / reg 5.000000e-03 / h 96 / train_accurancy 0.504224 / val_accurancy 0.490000\n",
      "lr 1.000000e-03 / reg 1.000000e-02 / h 96 / train_accurancy 0.508959 / val_accurancy 0.482000\n",
      "lr 2.500000e-03 / reg 1.000000e-04 / h 96 / train_accurancy 0.497347 / val_accurancy 0.452000\n",
      "lr 2.500000e-03 / reg 5.000000e-04 / h 96 / train_accurancy 0.505898 / val_accurancy 0.482000\n",
      "lr 2.500000e-03 / reg 1.000000e-03 / h 96 / train_accurancy 0.511143 / val_accurancy 0.501000\n",
      "lr 2.500000e-03 / reg 5.000000e-03 / h 96 / train_accurancy 0.497000 / val_accurancy 0.460000\n",
      "lr 2.500000e-03 / reg 1.000000e-02 / h 96 / train_accurancy 0.503000 / val_accurancy 0.481000\n",
      "Best val_accurancy is 0.501000\n"
     ]
    }
   ],
   "source": [
    "best_net = None # store the best model into this \n",
    "\n",
    "#################################################################################\n",
    "# TODO: Tune hyperparameters using the validation set. Store your best trained  #\n",
    "# model in best_net.                                                            #\n",
    "#                                                                               #\n",
    "# To help debug your network, it may help to use visualizations similar to the  #\n",
    "# ones we used above; these visualizations will have significant qualitative    #\n",
    "# differences from the ones we saw above for the poorly tuned network.          #\n",
    "#                                                                               #\n",
    "# Tweaking hyperparameters by hand can be fun, but you might find it useful to  #\n",
    "# write code to sweep through possible combinations of hyperparameters          #\n",
    "# automatically like we did on the previous exercises.                          #\n",
    "#################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "best_val = 0.0\n",
    "\n",
    "\n",
    "for hidden_size in [32, 48, 64, 80, 96, 128]:\n",
    "    for lr in [5e-4, 7.5e-4, 1e-3, 2.5e-3]:\n",
    "        for reg in [1e-4, 5e-4, 1e-3, 5e-3, 1e-2]:\n",
    "            net = TwoLayerNet(input_size=input_size, hidden_size=hidden_size, output_size=num_classes)\n",
    "            net.train(X_train, y_train, X_val, y_val, \n",
    "                      learning_rate=lr, learning_rate_decay=0.95, \n",
    "                      reg=reg, \n",
    "                      num_iters=1000, batch_size=200)\n",
    "            train_acc = np.mean(net.predict(X_train) == y_train)\n",
    "            val_acc = np.mean(net.predict(X_val) == y_val)\n",
    "            \n",
    "            if val_acc > best_val:\n",
    "                best_val = val_acc\n",
    "                best_net = net\n",
    "        \n",
    "            print(\"lr %e / reg %e / h %d / train_accurancy %f / val_accurancy %f\" % (lr, reg, hidden_size, train_acc, val_acc))\n",
    "\n",
    "print('Best val_accurancy is %f' % (best_val))\n",
    "\n",
    "pass\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "id": "val_accuracy"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation accuracy:  0.51\n"
     ]
    }
   ],
   "source": [
    "# Print your validation accuracy: this should be above 48%\n",
    "val_acc = (best_net.predict(X_val) == y_val).mean()\n",
    "print('Validation accuracy: ', val_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the weights of the best network\n",
    "show_net_weights(best_net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run on the test set\n",
    "When you are done experimenting, you should evaluate your final trained network on the test set; you should get above 48%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "id": "test_accuracy"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy:  0.51\n"
     ]
    }
   ],
   "source": [
    "# Print your test accuracy: this should be above 48%\n",
    "best_net = TwoLayerNet(input_size=input_size, hidden_size=80, output_size=num_classes)\n",
    "best_net.train(X_train, y_train, X_val, y_val, \n",
    "          learning_rate=1e-3, learning_rate_decay=0.95, \n",
    "          reg=1e-2, \n",
    "          num_iters=1960, batch_size=200)\n",
    "test_acc = (best_net.predict(X_test) == y_test).mean()\n",
    "print('Test accuracy: ', test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "**Inline Question**\n",
    "\n",
    "Now that you have trained a Neural Network classifier, you may find that your testing accuracy is much lower than the training accuracy. In what ways can we decrease this gap? Select all that apply.\n",
    "\n",
    "1. Train on a larger dataset.\n",
    "2. Add more hidden units.\n",
    "3. Increase the regularization strength.\n",
    "4. None of the above.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ 3\n",
    "\n",
    "$\\color{blue}{\\textit Your Explanation:}$\n",
    "\n",
    "- 可能是模型过于复杂导致过拟合\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
